Your responsibilities

Data Infrastructure Engineering
• Design, build, and operate backend systems that rely on scalable and highly available data persistence layers.
• Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability.
• Improve the reliability and performance of production datastores used by critical services.
Data Performance & Optimization
• Partner with service teams to improve database schema design, query performance, and data modelling.
• Optimize data access patterns and indexing strategies for relational and NoSQL databases.
• Support teams in designing systems that scale efficiently under high load.
Developer Experience & Platform Tooling
• Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers.
• Contribute to infrastructure automation using tools such as Terraform and internal developer platforms.
• Improve observability and operational insight into datastore performance and reliability.
Platform Reliability & Observability
• Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems.
• Develop autoscaling and performance optimization strategies for critical data infrastructure.
• Support operational excellence by reducing manual processes and improving system resilience.

Our requirements

5+ years of experience in software engineering, building and operating production systems
Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin)
Experience working with large-scale, data-intensive systems
Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency)
Experience working in cloud environments (preferably AWS)
Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch)
Hands-on experience with large-scale data pipelines and data processing systems
Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows)
Understanding of end-to-end data flow: ingestion (how data enters the system); transformation (how it is processed); storage & access (how other services consume it)
Experience designing systems where data performance, scalability, and reliability are critical